


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=1_3     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=2_2_4     ,   __snake_case=1_0_0_0     ,   __snake_case=[3, 3, 6, 4]     ,   __snake_case=[4_8, 5_6, 1_1_2, 2_2_0]     ,   ):
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							num_channels
         snake_case											=							is_training
         snake_case											=							use_labels
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							num_labels
         snake_case											=							image_size
         snake_case											=							layer_depths
         snake_case											=							embed_dims
    def 						a_     (			self       ):
         snake_case											=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         snake_case											=							None
         if self.use_labels:
              snake_case											=							ids_tensor([self.batch_size]     ,   self.num_labels       )

         snake_case											=							self.get_config()

         return config, pixel_values, labels
    def 						a_     (			self       ):
         return SwiftFormerConfig(
             depths=self.layer_depths     ,   embed_dims=self.embed_dims     ,   mlp_ratio=4     ,   downsamples=[True, True, True, True]     ,   hidden_act='''gelu'''     ,   num_labels=self.num_labels     ,   down_patch_size=3     ,   down_stride=2     ,   down_pad=1     ,   drop_rate=0.0     ,   drop_path_rate=0.0     ,   use_layer_scale=__snake_case     ,   layer_scale_init_value=1E-5     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							SwiftFormerModel(config=__snake_case       )
         model.to(__snake_case       )
         model.eval()
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.embed_dims[-1], 7, 7)       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.num_labels
         snake_case											=							SwiftFormerForImageClassification(__snake_case       )
         model.to(__snake_case       )
         model.eval()
         snake_case											=							model(__snake_case     ,   labels=__snake_case       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)       )

         snake_case											=							SwiftFormerForImageClassification(__snake_case       )
         model.to(__snake_case       )
         model.eval()

         snake_case											=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)       )




    def 						a_     (			self       ):
         ((snake_case)					,    (snake_case)					,    (snake_case))											=							self.prepare_config_and_inputs()
         snake_case											=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class     A__   (       snake_case__			,   snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    __magic_name__         =						(
        {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    def 						a_     (			self       ):
         snake_case											=							SwiftFormerModelTester(self       )
         snake_case											=							ConfigTester(
             self     ,   config_class=__snake_case     ,   has_text_modality=__snake_case     ,   hidden_size=3_7     ,   num_attention_heads=1_2     ,   num_hidden_layers=1_2     ,   )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              snake_case											=							model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__snake_case     ,   nn.Linear       )       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              snake_case											=							inspect.signature(model.forward       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case											=							[*signature.parameters.keys()]

              snake_case											=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case       )
    @slow
    def 						a_     (			self       ):
         for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case											=							SwiftFormerModel.from_pretrained(__snake_case       )
              self.assertIsNotNone(__snake_case       )
    @unittest.skip(reason='''SwiftFormer does not output attentions'''       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         def check_hidden_states_output(__snake_case     ,   __snake_case     ,   __snake_case       ):
              snake_case											=							model_class(__snake_case       )
              model.to(__snake_case       )
              model.eval()

              with torch.no_grad():
                   snake_case											=							model(**self._prepare_for_class(__snake_case     ,   __snake_case       )       )

              snake_case											=							outputs.hidden_states

              snake_case											=							8
              self.assertEqual(len(__snake_case       )     ,   __snake_case       )  # TODO

              # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
              # with the width and height being successively divided by 2, after every 2 blocks
              for i in range(len(__snake_case       )       ):
                   self.assertEqual(
                       hidden_states[i].shape     ,   torch.Size(
                           [
                               self.model_tester.batch_size,
                               self.model_tester.embed_dims[i // 2],
                               (self.model_tester.image_size // 4) // 2 ** (i // 2),
                               (self.model_tester.image_size // 4) // 2 ** (i // 2),
                           ]       )     ,   )

         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							True
              check_hidden_states_output(__snake_case     ,   __snake_case     ,   __snake_case       )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              snake_case											=							True

              check_hidden_states_output(__snake_case     ,   __snake_case     ,   __snake_case       )
    def 						a_     (			self       ):
         def _config_zero_init(__snake_case       ):
              snake_case											=							copy.deepcopy(__snake_case       )
              for key in configs_no_init.__dict__.keys():
                   if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                        setattr(__snake_case     ,   __snake_case     ,   1E-10       )
                   if isinstance(getattr(__snake_case     ,   __snake_case     ,   __snake_case       )     ,   __snake_case       ):
                        snake_case											=							_config_zero_init(getattr(__snake_case     ,   __snake_case       )       )
                        setattr(__snake_case     ,   __snake_case     ,   __snake_case       )
              return configs_no_init

         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         snake_case											=							_config_zero_init(__snake_case       )
         for model_class in self.all_model_classes:
              snake_case											=							model_class(config=__snake_case       )
              for name, param in model.named_parameters():
                   if param.requires_grad:
                        self.assertIn(
                            ((param.data.mean() * 1E9) / 1E9).round().item()     ,   [0.0, 1.0]     ,   msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,   )




    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
    def 						a_     (			self       ):
         pass






def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image

@require_torch
@require_vision
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    @cached_property
    def 						a_     (			self       ):
         return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''       ) if is_vision_available() else None




    @slow
    def 						a_     (			self       ):
         snake_case											=							SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''       ).to(__snake_case       )

         snake_case											=							self.default_image_processor
         snake_case											=							prepare_img()
         snake_case											=							image_processor(images=__snake_case     ,   return_tensors='''pt'''       ).to(__snake_case       )

         # forward pass
         with torch.no_grad():
              snake_case											=							model(**__snake_case       )

         # verify the logits
         snake_case											=							torch.Size((1, 1_0_0_0)       )
         self.assertEqual(outputs.logits.shape     ,   __snake_case       )

         snake_case											=							torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]       ).to(__snake_case       )
         self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,   __snake_case     ,   atol=1E-4       )       )








from functools import lru_cache
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							2
     snake_case											=							set()
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.add(UpperCamelCase_							)
     if n > 1:
          factors.add(UpperCamelCase_							)
     return factors
@lru_cache
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     return len(unique_prime_factors(UpperCamelCase_							)							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     return len(set(UpperCamelCase_							)							) in (0, 1)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							2

     while True:
          # Increment each value of a generated range
          snake_case											=							[base + i for i in range(UpperCamelCase_							)]

          # Run elements through out unique_prime_factors function
          # Append our target number to the end.
          snake_case											=							[upf_len(UpperCamelCase_							) for x in group]
          checker.append(UpperCamelCase_							)

          # If all numbers in the list are equal, return the group variable.
          if equality(UpperCamelCase_							):
               return group

          # Increment our base variable by 1
          base += 1
def      UpperCAmelCase__		(UpperCamelCase_ = 4							):



     """simple docstring"""






     snake_case											=							run(UpperCamelCase_							)
     return results[0] if len(UpperCamelCase_							) else None


if __name__ == "__main__":
 print(solution())




